The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
L'identification des nœuds critiques revêt une grande importance pour la protection des réseaux électriques. L'efficacité du réseau peut être utilisée comme indice d'évaluation pour identifier les nœuds critiques et constitue un indicateur permettant de quantifier l'efficacité avec laquelle un réseau échange des informations et transmet de l'énergie. Le réseau électrique étant un réseau hétérogène et pouvant être décomposé en petits réseaux fonctionnellement indépendants, le concept de composant géant ne s’applique pas aux réseaux électriques. Dans cet article, nous modélisons d’abord le réseau électrique sous forme de graphe orienté et définissons le sous-graphe d’efficacité géante (GEsG). Le GEsG est l'unité fonctionnellement indépendante du réseau où l'énergie électrique peut être transmise d'un nœud de production (c'est-à-dire les centrales électriques) à certains nœuds de demande (c'est-à-dire les stations de transport et les stations de distribution) via le chemin le plus court. Deuxièmement, nous proposons un algorithme pour évaluer l'importance des nœuds en calculant leur degré critique, dont les résultats peuvent être utilisés pour identifier les nœuds critiques dans des réseaux hétérogènes. Troisièmement, nous définissons la perte d'efficacité des nœuds pour vérifier l'exactitude de l'algorithme d'identification des nœuds critiques (CNI) et comparons les résultats selon lesquels GEsG et Giant Component sont utilisés séparément comme critères d'évaluation pour calculer la perte d'efficacité des nœuds. Les expériences prouvent la précision et l'efficacité de notre algorithme CNI et montrent que le GEsG peut mieux refléter les caractéristiques hétérogènes et la transmission de puissance des réseaux électriques que le composant géant. Notre enquête conduit à une découverte contre-intuitive selon laquelle les nœuds critiques les plus importants ne sont peut-être pas les nœuds de génération mais certains nœuds de demande.
WenJie KANG
National University of Defense Technology
PeiDong ZHU
Changsha University
JieXin ZHANG
National University of Defense Technology
JunYang ZHANG
National University of Defense Technology
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WenJie KANG, PeiDong ZHU, JieXin ZHANG, JunYang ZHANG, "Critical Nodes Identification of Power Grids Based on Network Efficiency" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 11, pp. 2762-2772, November 2018, doi: 10.1587/transinf.2018EDP7042.
Abstract: Critical nodes identification is of great significance in protecting power grids. Network efficiency can be used as an evaluation index to identify the critical nodes and is an indicator to quantify how efficiently a network exchanges information and transmits energy. Since power grid is a heterogeneous network and can be decomposed into small functionally-independent grids, the concept of the Giant Component does not apply to power grids. In this paper, we first model the power grid as the directed graph and define the Giant Efficiency sub-Graph (GEsG). The GEsG is the functionally-independent unit of the network where electric energy can be transmitted from a generation node (i.e., power plants) to some demand nodes (i.e., transmission stations and distribution stations) via the shortest path. Secondly, we propose an algorithm to evaluate the importance of nodes by calculating their critical degree, results of which can be used to identify critical nodes in heterogeneous networks. Thirdly, we define node efficiency loss to verify the accuracy of critical nodes identification (CNI) algorithm and compare the results that GEsG and Giant Component are separately used as assessment criteria for computing the node efficiency loss. Experiments prove the accuracy and efficiency of our CNI algorithm and show that the GEsG can better reflect heterogeneous characteristics and power transmission of power grids than the Giant Component. Our investigation leads to a counterintuitive finding that the most important critical nodes may not be the generation nodes but some demand nodes.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7042/_p
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@ARTICLE{e101-d_11_2762,
author={WenJie KANG, PeiDong ZHU, JieXin ZHANG, JunYang ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Critical Nodes Identification of Power Grids Based on Network Efficiency},
year={2018},
volume={E101-D},
number={11},
pages={2762-2772},
abstract={Critical nodes identification is of great significance in protecting power grids. Network efficiency can be used as an evaluation index to identify the critical nodes and is an indicator to quantify how efficiently a network exchanges information and transmits energy. Since power grid is a heterogeneous network and can be decomposed into small functionally-independent grids, the concept of the Giant Component does not apply to power grids. In this paper, we first model the power grid as the directed graph and define the Giant Efficiency sub-Graph (GEsG). The GEsG is the functionally-independent unit of the network where electric energy can be transmitted from a generation node (i.e., power plants) to some demand nodes (i.e., transmission stations and distribution stations) via the shortest path. Secondly, we propose an algorithm to evaluate the importance of nodes by calculating their critical degree, results of which can be used to identify critical nodes in heterogeneous networks. Thirdly, we define node efficiency loss to verify the accuracy of critical nodes identification (CNI) algorithm and compare the results that GEsG and Giant Component are separately used as assessment criteria for computing the node efficiency loss. Experiments prove the accuracy and efficiency of our CNI algorithm and show that the GEsG can better reflect heterogeneous characteristics and power transmission of power grids than the Giant Component. Our investigation leads to a counterintuitive finding that the most important critical nodes may not be the generation nodes but some demand nodes.},
keywords={},
doi={10.1587/transinf.2018EDP7042},
ISSN={1745-1361},
month={November},}
Copier
TY - JOUR
TI - Critical Nodes Identification of Power Grids Based on Network Efficiency
T2 - IEICE TRANSACTIONS on Information
SP - 2762
EP - 2772
AU - WenJie KANG
AU - PeiDong ZHU
AU - JieXin ZHANG
AU - JunYang ZHANG
PY - 2018
DO - 10.1587/transinf.2018EDP7042
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2018
AB - Critical nodes identification is of great significance in protecting power grids. Network efficiency can be used as an evaluation index to identify the critical nodes and is an indicator to quantify how efficiently a network exchanges information and transmits energy. Since power grid is a heterogeneous network and can be decomposed into small functionally-independent grids, the concept of the Giant Component does not apply to power grids. In this paper, we first model the power grid as the directed graph and define the Giant Efficiency sub-Graph (GEsG). The GEsG is the functionally-independent unit of the network where electric energy can be transmitted from a generation node (i.e., power plants) to some demand nodes (i.e., transmission stations and distribution stations) via the shortest path. Secondly, we propose an algorithm to evaluate the importance of nodes by calculating their critical degree, results of which can be used to identify critical nodes in heterogeneous networks. Thirdly, we define node efficiency loss to verify the accuracy of critical nodes identification (CNI) algorithm and compare the results that GEsG and Giant Component are separately used as assessment criteria for computing the node efficiency loss. Experiments prove the accuracy and efficiency of our CNI algorithm and show that the GEsG can better reflect heterogeneous characteristics and power transmission of power grids than the Giant Component. Our investigation leads to a counterintuitive finding that the most important critical nodes may not be the generation nodes but some demand nodes.
ER -